Urban Avian Conservation Planning Using Species Functional Traits and Habitat Suitability Mapping
Bibliographic record
Abstract
Urbanization adversely impacts biodiversity by reducing the quantity and quality of natural habitat areas. Additionally, the quality of natural habitat depends on its bio-physical characteristics (e.g., natural cover, impervious surfaces, urban tree canopy) as well as the functional traits of species inhabiting them (e.g., breeding/foraging habitat requirements). To better plan conservation of regional biodiversity in urbanized landscapes, it is therefore critical to assess the relationship between the landscape and the response of key Functional Trait Groups (FTGs) of species. To identify different FTGs of 116 avian species in the urbanized landscape of the Toronto region (Canada), we conducted a Functional Trait Analysis (FTA) using RLQ-fourth corner analysis. We focused on four species traits (diet, foraging, nesting, and territoriality) to identify the FTGs and their association with natural cover and landscape characteristics (landcover types, patch quality, habitat connectivity). Then, to predict FTG presence in relation to the landscape characteristics, we performed a Habitat Suitability Analysis (HSA). From this analysis, we found 21 avian FTGs with different habitat suitability values that correspond to forested patches and wetlands. The HSA for tree canopy, forest insectivore, and ground-nesting birds (or FTGs) have higher suitability values within forest patches, while aerial insectivores have higher suitability values in older residential neighborhoods indicating the value of the urban tree canopy. This methodological approach shows that by mapping habitat suitability by FTG one can identify strategic conservation areas that target multiple species, shifting efforts from a single species to a community-based functional focus. Our study highlights the conservation value of remnant and/or restored habitat patches in near urban and urban landscapes that help to maximize the persistence of regional avian biodiversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".